Hybrid method integrating agent-based modeling and heuristic tree search for scheduling of complex batch processes

• Efficient method for solving complex industrial batch scheduling problems.
• Adaptable to complex constraints including non-standard ones.
• Tradeoff between solution efficiency and schedule performance.
• Comparison with agent-based modeling and mixed-integer programing.

We propose a hybrid method integrating agent-based modeling and heuristic tree search to solve complex batch scheduling problems. Agent-based modeling describes the batch process and constructs a feasible schedule under various constraints. To overcome myopic decisions of agents, the agent-based simulation is embedded into a heuristic search algorithm. The heuristic algorithm partially explores the solution space generated by the agent-based simulation. Because global information of the objective function value is used in the search algorithm, the schedule performance is improved. The proposed method shares the advantages from both agent-based modeling and mixed integer programing, achieving a better balance between the solution efficiency and the schedule performance. As a polynomial-time algorithm, the hybrid method is applicable to large-scale complex industrial scheduling problems. Its performance is demonstrated by comparing with agent-based modeling and mixed integer programing in two case studies, including a complex one from The Dow Chemical Company.

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